Describing Data Graphically with R

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# Describing Data Graphically with R
### S. Mason Garrison

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<div class="my-footer">
<span>
<a href="https://DataScience4Psych.github.io/DataScience4Psych/" target="_blank">Methods in Psychological Research</a>
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</div>

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# Describing Data

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## Hans Rosling

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# Summarize

.pull-left[

- Transform a pile of numbers into a summary
- Descriptive Statistics
    - Distribution of a variable is a table/graph showing the categories/values of outcomes and their frequency/percentage of occurrence
    - Exploratory Data Analysis (Tukey, 1977)
]
.pull-right[

![](data:image/png;base64,#../img/numberpile.png)
]

---

# Exploratory Data Analysis
.pull-left[
- Tukey (1977)
    -	EDA
    - Graphical Data Analysis
    - Numbers as summaries
    - Emphasized Robust Statistics
]
--
.pull-right[
![](data:image/png;base64,#../img/tukey.png)
]

---

# Descriptive Statistics
- Examples
    - Tables
    - Graphs
    - Summary Statistics

---

# Tables

- Woodbridge (1845)

![](data:image/png;base64,#../img/woodbridge1845.png)
     
---

# Graphs
  -  Minard (1869)

![](data:image/png;base64,#../img/minard.png)

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# Examples

- Summary Statistics
    - Measures of Central Tendency
    - Measures of Spread

![](data:image/png;base64,#../img/centralbears.jpg)

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# Categorical Variable Displays (Nominal, Ordinal)

- Frequency Distribution Graphs
    - Bar Chart
    - Pie Chart
- Quantitative Variables
    - Histograms
    - Stem plots
- Time Plots

---

# Frequency distribution graph

.pull-left[
- Bar Chart
    - Graphs of variables with categories of outcomes on the x axis; and the frequency or percent of each category on the Y axis.
 
]
.pull-right[

![Nicole Oresme (Bishop of Lisieus) circa 1350](data:image/png;base64,#../img/oresme.jpg)
]

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# Bar Graph/Chart
.pull-left[

```r
# Bar chart
library(car)
counts <- table(mtcars$gear)
```
]
.pull-right[

```r
barplot(counts, main="Car Distribution",
  	xlab="Number of Gears")
```

<img src="data:image/png;base64,#descriptive_files/figure-html/unnamed-chunk-4-1.png" width="90%" style="display: block; margin: auto;" />
]
---

# Stacked Bar Chart
.pull-left[

```r
df <- data.frame(
  group = c("Male", 
            "Female", 
            "Child"),
  value = c(25, 25, 50)
  )
head(df)
```

```
##    group value
## 1   Male    25
## 2 Female    25
## 3  Child    50
```
]
.pull-right[

```r
library(ggplot2)
(bp=ggplot(df, aes(x="",y=value, fill=group))+
geom_bar(width = 1, stat = "identity"))
```

<img src="data:image/png;base64,#descriptive_files/figure-html/unnamed-chunk-6-1.png" width="90%" style="display: block; margin: auto;" />
]
---

# Pie Chart
.pull-left[
- Graphs of variables with categories of outcomes as frequency or percent of each category in the pie.
]

.pull-right[

![A pie chart showing each state in the United States, part of Playfair's translation of A Statistical Account of the United States of America by D. F. Donnant.](data:image/png;base64,#../img/pieplay.jpg)
]

---

# Pie chart

.pull-left[

```r
slices <- c(10, 12,4, 16, 8)
lbls <- c("US", "UK", "Australia", "Germany", "France")
```
]
.pull-right[

```r
pie(slices, labels = lbls, main="Pie Chart of Countries")
```

<img src="data:image/png;base64,#descriptive_files/figure-html/unnamed-chunk-8-1.png" width="90%" style="display: block; margin: auto;" />
]
---

# Example 2
.small[

```r
mytable <- table(iris$Species)
lbls <- paste(names(mytable), "\n", mytable, sep="")
pie(mytable, labels = lbls,
		main="Pie Chart of Species\n (with sample sizes)")
```

<img src="data:image/png;base64,#descriptive_files/figure-html/unnamed-chunk-9-1.png" width="90%" style="display: block; margin: auto;" />
]
---

# Convert Bar Chart into Pie Chart
.small[

```r
pie <- bp + coord_polar("y", start=0)
pie
```

```r
pie + scale_fill_manual(values=c("#999999", "#E69F00", "#56B4E9"))
```

<img src="data:image/png;base64,#descriptive_files/figure-html/unnamed-chunk-10-2.png" width="90%" style="display: block; margin: auto;" />
]
[Additional Resources](http://www.sthda.com/english/wiki/ggplot2-pie-chart-quick-start-guide-r-software-and-data-visualization)

---

# Quantitative Variables

- Interval or Ratio Scales
    - Histograms
    - Stem plots
    - Time plots

---

# Histogram 
- A histogram is a graphical representation of the distribution of numerical data.
    - Approximates a probability distribution
    - First described in @Pearson1895.
    
---
# Histogram
.pull-left[

```r
library(MASS)
variable<-cats$Bwt
hist(variable)
```

<img src="data:image/png;base64,#descriptive_files/figure-html/unnamed-chunk-11-1.png" width="90%" style="display: block; margin: auto;" />
]

.pull-right[

```r
variable<-variable*2.2   #Convert to Imperial
hist(variable)
```

<img src="data:image/png;base64,#descriptive_files/figure-html/unnamed-chunk-12-1.png" width="90%" style="display: block; margin: auto;" />
]
---

# Stemplot

- Sometimes called a stem and leaf diagram

```r
# Stem and Leaf plot
stem(faithful$eruptions,scale=1)
```

```
## 
##   The decimal point is 1 digit(s) to the left of the |
## 
##   16 | 070355555588
##   18 | 000022233333335577777777888822335777888
##   20 | 00002223378800035778
##   22 | 0002335578023578
##   24 | 00228
##   26 | 23
##   28 | 080
##   30 | 7
##   32 | 2337
##   34 | 250077
##   36 | 0000823577
##   38 | 2333335582225577
##   40 | 0000003357788888002233555577778
##   42 | 03335555778800233333555577778
##   44 | 02222335557780000000023333357778888
##   46 | 0000233357700000023578
##   48 | 00000022335800333
##   50 | 0370
```

---

# Time Plots

- Edward Tufte has said that Minard's plot:

> "may well be the best statistical graphic ever drawn"

- It packs a ton of information into one dense figure.

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# Time Plots

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- The plot contains six variables, each mapped to a different aesthetic:

| Information                           | Aesthetic       |
|---------------------------------------|-----------------|
| Size of Napoleon's Grande Armée       | Width of path   |
| Longitude of the army's position      | x-axis          |
| Latitude of the army's position       | y-axis          |
| Direction of the army's movement      | Color of path   |
| Date of points along retreat path     | Text below plot |
| Temperature during the army's retreat | Line below plot |

---

# Recreation in R
- This plot has been recreated in R by:
    - [Andrew Heiss](https://www.andrewheiss.com/blog/2017/08/10/exploring-minards-1812-plot-with-ggplot2/)
    - [Michael Friendly](http://www.datavis.ca/gallery/re-minard.php)
    - @Wickham2010 [link](https://www.tandfonline.com/doi/suppl/10.1198/jcgs.2009.07098?scroll=top)

---

# Side by Side

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# More Accessible Resources

- [R Graph Catalog](http://shinyapps.stat.ubc.ca/r-graph-catalog/)

- [intRo](http://www.intro-stats.com/)

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# R Basics

- Installation
  - R can be downloaded from one of the mirror sites in http://cran.r-project.org/mirrors.html. You should pick your nearest location.

- Using External Data
  - R offers plenty of options for loading external data, including Excel, Minitab and SPSS files.

- R Session
  - After R is started, there is a console awaiting for input. At the prompt (>), you can enter numbers and perform calculations.

> 1 + 2 
[1] 3

---

# Variable Assignment

We assign values to variables with the assignment operator "=". Just typing the variable by itself at the prompt will print out the value. We should note that another form of assignment operator "<-" is also in use.

```
## [1] 1
```

# Functions

R functions are invoked by its name, then followed by the parenthesis, and zero or more arguments. The following apply the function c to combine three numeric values into a vector.

```
## [1] 1 2 3
```
---

# Comments
All text after the pound sign "#" within the same line is considered a comment.

> 1 + 1      # this is a comment 
[1] 2

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# Extension Package
Sometimes we need additional functionality beyond those offered by the core R library. In order to install an extension package, you should invoke the install.packages function at the prompt and follow the instruction.
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#
> install.packages()
Getting Help
R provides extensive documentation. For example, entering ?c or help(c) at the prompt gives documentation of the function c in R. Please give it a try.

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#
> help(c)
If you are not sure about the name of the function you are looking for, you can perform a fuzzy search with the apropos function.

> apropos("nova") 
[1] "anova"                "anova.glm" 
   ....

# References

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# Wrapping Up...

<br><br>
![](img/validreliable.gif)